diff --git a/notebooks/ml_grad_boost_tree.ipynb b/notebooks/ml_grad_boost_tree.ipynb
index 341b439..fcddf8f 100644
--- a/notebooks/ml_grad_boost_tree.ipynb
+++ b/notebooks/ml_grad_boost_tree.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
     "import matplotlib.pyplot as plt\n",
     "import xgboost as xgb\n",
     "from sklearn.model_selection import GridSearchCV\n",
-    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.metrics import confusion_matrix, f1_score\n",
     "from sklearn.ensemble import GradientBoostingClassifier\n",
     "from sklearn.impute import SimpleImputer\n",
     "from sklearn.metrics import accuracy_score\n",
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -138,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -171,29 +171,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: total: 3min 28s\n",
-      "Wall time: 4min 16s\n"
+      "CPU times: total: 2min 49s\n",
+      "Wall time: 4min 28s\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=GradientBoostingClassifier(),\n",
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=GradientBoostingClassifier(),\n",
        "             param_grid={&#x27;learning_rate&#x27;: [0.1, 0.2, 0.3],\n",
        "                         &#x27;max_depth&#x27;: [1, 3, 5],\n",
        "                         &#x27;n_estimators&#x27;: [100, 200, 300]},\n",
-       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=GradientBoostingClassifier(),\n",
+       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=GradientBoostingClassifier(),\n",
        "             param_grid={&#x27;learning_rate&#x27;: [0.1, 0.2, 0.3],\n",
        "                         &#x27;max_depth&#x27;: [1, 3, 5],\n",
        "                         &#x27;n_estimators&#x27;: [100, 200, 300]},\n",
-       "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
+       "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
       ],
       "text/plain": [
        "GridSearchCV(cv=3, estimator=GradientBoostingClassifier(),\n",
@@ -203,7 +203,7 @@
        "             scoring='accuracy')"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -223,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -231,7 +231,7 @@
      "output_type": "stream",
      "text": [
       "Best parameters: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100}\n",
-      "Best score: 0.796973125095374\n"
+      "Best score: 0.7969733696438982\n"
      ]
     }
    ],
@@ -251,16 +251,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['../ml_models/best_gbt_model_20240611203442.joblib']"
+       "['../ml_models/best_gbt_model_20240612171757.joblib']"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -290,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -304,19 +304,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "GradientBoostingClassifier()"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -330,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -356,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -388,12 +388,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -422,7 +422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -461,6 +461,26 @@
     "plt.tight_layout()\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Score: 0.8004770573896727\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate F1 Score for multiclass classification\n",
+    "f1 = f1_score(test_y, preds, average='macro')\n",
+    "\n",
+    "print('F1 Score:', f1)"
+   ]
   }
  ],
  "metadata": {
diff --git a/notebooks/ml_xgboost.ipynb b/notebooks/ml_xgboost.ipynb
index 4c8bc60..3c17c45 100644
--- a/notebooks/ml_xgboost.ipynb
+++ b/notebooks/ml_xgboost.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,10 +20,9 @@
     "import matplotlib.pyplot as plt\n",
     "import xgboost as xgb\n",
     "from sklearn.model_selection import GridSearchCV\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.preprocessing import MinMaxScaler\n",
-    "import seaborn as sns"
+    "from sklearn.metrics import confusion_matrix, f1_score\n",
+    "import seaborn as sns\n",
+    "import numpy as np"
    ]
   },
   {
@@ -35,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -337,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -362,14 +361,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[20:16:51] WARNING: C:/Users/administrator/workspace/xgboost-win64_release_1.6.0/src/learner.cc:627: \n",
+      "[16:58:49] WARNING: C:/Users/administrator/workspace/xgboost-win64_release_1.6.0/src/learner.cc:627: \n",
       "Parameters: { \"best_iteration\", \"best_ntree_limit\", \"scikit_learn\" } might not be used.\n",
       "\n",
       "  This could be a false alarm, with some parameters getting used by language bindings but\n",
@@ -377,13 +376,7 @@
       "  but getting flagged wrongly here. Please open an issue if you find any such cases.\n",
       "\n",
       "\n",
-      "[0]\ttrain-merror:0.16762\teval-merror:0.22603\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "[0]\ttrain-merror:0.16762\teval-merror:0.22603\n",
       "[1]\ttrain-merror:0.15220\teval-merror:0.22374\n",
       "[2]\ttrain-merror:0.13849\teval-merror:0.21461\n",
       "[3]\ttrain-merror:0.13535\teval-merror:0.20776\n",
@@ -483,8 +476,8 @@
       "[97]\ttrain-merror:0.00029\teval-merror:0.18265\n",
       "[98]\ttrain-merror:0.00029\teval-merror:0.18265\n",
       "[99]\ttrain-merror:0.00029\teval-merror:0.18265\n",
-      "CPU times: total: 17.6 s\n",
-      "Wall time: 1.36 s\n"
+      "CPU times: total: 14.3 s\n",
+      "Wall time: 1.22 s\n"
      ]
     }
    ],
@@ -506,7 +499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -546,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -577,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -609,7 +602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -618,7 +611,7 @@
        "<AxesSubplot:title={'center':'Feature importance'}, xlabel='F score', ylabel='Features'>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -640,7 +633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -679,6 +672,26 @@
     "plt.tight_layout()\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Score: 0.8157211953487169\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate F1 Score for multiclass classification\n",
+    "f1 = f1_score(test_y, preds, average='macro')\n",
+    "\n",
+    "print('F1 Score:', f1)"
+   ]
   }
  ],
  "metadata": {